Implementation.
We set patch size \(L\)  to 12.
We set the number of patches \(P\)  to 168 for METR-LA and PEMS-BAY, and 336 for PEMS04, i.e., we use historical information for a week for METR-LA and PEMS-BAY, and two weeks for PEMS04.
We aim at forecasting the next 12 time steps.
The masking ratio \(r\)  is set to 75%.
The hidden dimension of the latent representations of TSFormer \(d\)  is set to 96.
The TSFormer encoder uses 4 layer of Transformer blocks, and the decoder uses 1 layer.
The number of attention heads in Transformer blocks is set to 4.
The hyper-parameter of Graph WaveNet is set to default in their papers [1]}.
For the \(k\) NN graph \(\mathbf {A}^a\) , we set \(k\)  to 10.
We perform significance tests (t-test with p-value < 0.05) over all the experimental results.
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